Artificial Intelligence Engineer

Position Overview:

AI Engineer

Key Responsibilities:

Deep Learning & Neural Networks

• Build, train, and optimize deep learning models leveraging

  TensorFlow, PyTorch

  , and

  transformer-based architectures

  .
• Adapt and fine-tune pre-trained models for

  specific use cases

  involving diverse datasets across

  text, image, and audio

  .
• Enhance model performance on

  NVIDIA GPU systems

  (A100, H100) to enable efficient inference.
• Develop

  LLMOps and context-aware pipelines

  for chat-driven applications using

  Python frameworks

  .
• Collaborate closely with data science and product teams to bring models into real-world production systems.

API Development & Integration

• Design and maintain

  RESTful and gRPC APIs

  for model serving and data exchange.
• Oversee complete API lifecycle management, including

  version control, documentation, and security

  .
• Implement and integrate APIs into internal/external systems using

  FastAPI

  or related frameworks.

AWS Cloud Engineering

• Configure and manage

  AWS Container Apps, Container Registries

  , and

  Docker images

  for scalable deployments.
• Deploy, monitor, and optimize

  AWS-based web applications

  for hosting AI-driven dashboards and services.
• Build

  CI/CD automation pipelines

  through

  GitHub Actions

  to streamline continuous integration and deployment.

Required Qualifications:

1. 2+ years

   of experience in

   Python development

   focused on

   deep learning applications

   .
2. Hands-on expertise in

   transformers, Hugging Face

   , and

   custom model training workflows

   .
3. Proven experience in deploying models on

   GPU-backed environments

   .
4. Strong grasp of

   API design concepts

   and

   microservices architecture

   .
5. Practical knowledge of

   AWS services, Docker

   , and

   GitHub Actions

   for deployment automation.
6. Excellent analytical, communication, and problem-solving abilities with a team-oriented mindset.

Preferred Qualifications:

1. Exposure to

   multi-modal or Generative AI

   model development.
2. Understanding of

   MLOps tools, workflows, and best practices

   .
3. Prior contributions to

   open-source AI projects

   or

   academic research/publications

   in the field.